A Diameter and Hemodynamic Regulation Model Utilizing An Arteriolar Network Structural Model and Experiment-Based Machine Learning

Document Type

Article

Publication Date

10-1-2026

Publication Title

Function (Oxf)

Abstract

Arteriolar regulation is central to maintaining adequate tissue perfusion, and its disruption can lead to adverse health outcomes. However, mechanisms governing vessel responses to complex environmental conditions within a network context remain uncertain. This study presents computational simulations of arteriolar network responses to various combinations and intensities of five physiologically significant stimuli: metabolic (via adenosine), adrenergic (via norepinephrine), oxygen, myogenic (intralumenal pressure), and blood flow/shear stress. Individual vessel diameter adaptations are predicted using machine learning models trained on extensive experimental data of arteriolar responses to stimuli combinations in rat skeletal muscle. After evaluating multiple single vessel modeling pipelines, a high-performing machine learning vessel model (R

First Page

e0452026

DOI

10.1152/function.045.2026

ISSN

2633-8823

PubMed ID

42821495

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